Though graph neural networks are powerful tools to characterize graph-structured data, they heavily rely on fine-grained domain-specific labels while training, which is usually scarce and difficult to obtain.
To alleviate the above issues, pre-training for graph neural networks has drawn much attention recently, which empowers GNNs to capture the structural and semantic information of the input unlabeled graphs (or with few coarse-grained labels), followed by several fine-tuning steps on the downstream tasks of interest. Obviously, developing effective supervised (self-supervised) signals to guide GNNs to exploit structural and semantic properties on original graphs is at the heart.
Generally, existing designed supervised signals can be classified into two main categories.
The first is called node-level tasks, which aims at predicting localized properties utilizing node representations,
such as graph structure reconstruction [1]}, [2]}, [3]}, [4]}, [5]}, localized attribute prediction [2]}, [3]} and node representation recovery [8]}.
Another is called graph-level tasks, which defines globalized optimization goal for the entire graph,
such as graph property prediction [2]}, [10]} and mutual information maximization [11]}, [12]}, [13]}, [4]}, [15]}.
Our proposed framework differs from the above approaches in the following two aspects: learning node- and graph-level graph semantics in one single pre-training task and adaptive graph representations.
